On asymptotics of large Haar distributed unitary matrices

Let U _n be an n × n Haar unitary matrix. In this paper, the asymptotic normality and independence of Tr U _n , Tr U _n ² ,..., Tr U _n ^k are shown by using elementary methods. More generally, it is shown that the renormalized truncated Haar unitaries converge to a Gaussian random matrix in distribution. This revised version was published online in August 2006 with corrections to the Cover Date.     

On the matrices of given rank in a large subspace

Let V be a linear subspace of M_n,p(K) with codimension lesser than n, where K is an arbitrary field and n?p. In a recent work of the author, it was proven that V is always spanned by its rank p matrices unless n=p=2 and K?F₂. Here, we give a sufficient condition on codim V for V to be spanned by its rank r matrices for a given r∈?1,p-1?. This involves a generalization of the Gerstenhaber theorem on linear subspaces of nilpotent matrices.     

On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices

Let X_n be n×N containing i.i.d. complex entries and unit variance (sum of variances of real and imaginary parts equals 1), σ>0 constant, and R_n an n×N random matrix independent of X_n. Assume, almost surely, as n→∞, the empirical distribution function (e.d.f.) of the eigenvalues of converges in distribution to a nonrandom probability distribution function (p.d.f.), and the ratio tends to a positive number. Then it is shown that, almost surely, the e.d.f. of the eigenvalues of converges in distribution. The limit is nonrandom and is characterized in terms of its Stieltjes transform, which satisfies a certain equation.     

On finding robust approximate inverses for large sparse matrices

This paper presents a method based on matrix-matrix multiplication concepts for determining the approximate (sparse) inverses of sparse matrices. The suggested method is a development on the well-known Schulz iteration and it can successfully be combined with iterative solvers and sparse approximation techniques as well. A detailed discussion on the convergence rate of this scheme is furnished. Results of numerical experiments are also reported to illustrate the performance of the proposed method.     

On the stability and spectral radius of a finite set of matrices

This paper studies some problems related to the stability and the spectral radius of a finite set of matrices. A seasonal epidemic model is given to illustrate the use of the obtained results. In this example, the relationship between the obtained results and the stability of a discrete time periodic linear system is obtained.     

Weak stability for matrices

An n×n complex matrix A is called weak stable if there exists a matrix W such that W+W^* is positive definite and such that AW+W^*A^* is positive definite. In this note several characterizations for weak stability of a matrix are given, and conditions (on A) allowing W to be a diagonal matrix are also considered. A consequence of our results here is a characterization for nonsingular M-matrices.     

On the determinant of quaternionic polynomial matrices and its application to system stability

In this paper, we propose a definition of determinant for quaternionic polynomial matrices inspired by the well‐known Dieudonné determinant for the constant case. This notion allows to characterize the stability of linear dynamical systems with quaternionic coefficients, yielding results which generalize the ones obtained for the real and complex cases. Copyright © 2007 John Wiley & Sons, Ltd.     

On some spectral properties of large self-dual dilute quaternion random matrices

In this paper, we study the spectral properties of the large self-dual dilute quaternion random matrices. For the dilute case, we prove that the empirical spectral distribution still converges to the semicircular law with some appropriate normalization. Further, we obtain the limits of the extreme eigenvalues of the large self-dual dilute quaternion random matrices under some moment assumptions of the underlying distributions and give a necessary condition for the strong convergence of the extreme eigenvalues.     

Bounding the spectrum of large Hermitian matrices

Estimating upper bounds of the spectrum of large Hermitian matrices has long been a problem with both theoretical and practical significance. Algorithms that can compute tight upper bounds with minimum computational cost will have applications in a variety of areas. We present a practical algorithm that exploits k-step Lanczos iteration with a safeguard step. The k is generally very small, say 5-8, regardless of the large dimension of the matrices. This makes the Lanczos iteration economical. The safeguard step can be realized with marginal cost by utilizing the theoretical bounds developed in this paper. The bounds establish the theoretical validity of a previous bound estimator that has been successfully used in various applications. Moreover, we improve the bound estimator which can now provide tighter upper bounds with negligible additional cost.     

On solution of large systems of linear equations with block-Toeplitz banded matrices

A novel numerical algorithm is proposed for solving systems of linear equations with block-Toeplitz narrow-banded matrices. Its arithmetical cost is about double of that of the block cyclic reduction. The backward roundoff error stability of the method guarantees its reliability for the matrices that are not symmetric positive definite or diagonally dominant.     

On relative stability and weighted laws of large numbers

Weighted laws of large numbers are established for components which are independent copies of a positive relatively stable law and the weights comprise a regularly varying sequence. The index of regular variation of the weights must be at least ?1 for a weak law and be exactly ?1 for a strong law. Consideration is given to the special case where the truncated moment function is proportional to the logarithm, a case arising from quotients of independent random variables and quotients of successive order statistics. Closure of the class of positive relatively stable laws under independent multiplication (i.e, Mellin convolution) is reprised and tail equivalences are extended.     

Linear stability of some symplectic matrices

This paper is devoted to a study on the linear stability of some basic normal forms of symplectic matrices. Some necessary and sufficient conditions are given.     

On the eigenvalues of matrices

Summary Let A be an n×n matric with arbitrary complex elements and with eigen-values λ₁, λ₂, ..., λ_n. A method is described for the approximàte determination of max | λ_j | ; characteristical is that prescribing a percentual error the number of elementary operations of the process, necessary to reach such precision, depends only on n and not on the elements of A More general characteristical equations are also considered. To Enrico Bompiani on his scientific Jubilee     

ON EXPONENTIAL STABILITY OF LARGE SCALE SYSTEMS WITH UNBOUNDED DELAY

ＯＮＥＸＰＯＮＥＮＴＩＡＬＳＴＡＢＩＬＩＴＹＯＦＬＡＲＧＥＳＣＡＬＥＳＹＳＴＥＭＳＷＩＴＨＵＮＢＯＵＮＤＥＤＤＥＬＡＹＬＩＬＩＭＩＮＧ（李黎明）（ＨｅｂｅｉＩｎｓｔｉｔｕｔｅｏｆＦｉｎａｎｃｅａｎｄＥｃｏｎｏｍｉｃｓ，Ｓｈｉｊｉａｚｈｕａｎｇ０５００...     

On double-one matrices and double-zero matrices

We investigate two special classes of matrices over GF(2) with certain interesting properties. These properties can be applied to construct nonsingular matrix pairs efficiently and thus provide a solution to the long-key problems of McEIiece's public-key cryptosystem.     

On double-one matrices and double-zero matrices

We investigate two special classes of matrices over GF(2) with certain interesting properties. These properties can be applied to construct nonsingular matrix pairs efficiently and thus provide a solution to the long-key problems of McEIiece's public-key cryptosystem.